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Nursing Science 2025
在护理教育中使用生成式人工智能的范围审查
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Abstract:
目的：本研究旨在综合生成式人工智能在医学教育中的潜在机会、局限性和未来方向与前景，并使用这些来指导未来的探索领域。方法：对2022年以来发表的在医学教育背景下讨论生成式人工智能的英文文章进行了范围审查。使用PubMed、谷歌学术和sci-hub数据库进行文献检索。结果：主题分析揭示了生成式人工智能在医学教育中的多种潜在应用，包括自我导向学习、模拟场景和写作辅助。然而，文献也强调了重大挑战，如学术诚信、数据准确性和对学习的潜在危害等问题。针对各种机会和局限提出未来方向与前景。结论：生成式人工智能在医学教育中的整合带来了激动人心的机遇，同时也带来了巨大的挑战。有必要开发与人工智能相关的新技能和能力，以及深思熟虑、细致入微的方法来检查生成式人工智能在医学教育中日益增长的使用。
Objective: This study aims to synthesize the potential opportunities, limitations, and future directions and prospects of generative artificial intelligence in medical education, and use these to guide future areas of exploration. Methods: A scoping review was conducted of English articles published since 2022 discussing generative AI in the context of medical education. PubMed, Google Academic and sci-hub databases were used for literature search. Results: Thematic analysis revealed multiple potential applications of generative AI in medical education, including self-directed learning, simulation scenarios, and writing assistance. However, the literature also highlights significant challenges, such as issues of academic integrity, data accuracy, and potential harm to learning. Present future directions and perspectives for various opportunities and limitations. Conclusion: The integration of generative AI in medical education presents exciting opportunities as well as enormous challenges. There is a need to develop new skills and competencies related to AI, as well as thoughtful, nuanced approaches to examine the growing use of generative AI in medical education.
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